Large data sets may exist in various sizes and organizational structures. With big data comprising data sets as large as ever, the volume of data collected incident to the increased popularity of online and electronic transactions continues to grow. For example, billions of records (also referred to as rows) and hundreds of thousands of columns worth of data may populate a single table.
Users and business processes may interact with the large data sets in a variety of circumstances. For example, users and business processes may interact with and process large data sets through business process management (BPM) software. BPM software may enable users and business processes to link one or more data tables through the creation of business data objects. Each business data object may comprise a business data object class that links the business data object to various assignments, attachments, indexes, and/or other data tables. In response to a change to the business data object class (e.g., during a BPM software update, creation of a new business data object class, revision of a business data object class, etc.), the business data object and various linked data tables may need to be updated to ensure data integrity and prevent data corruption. The business data objects and/or various linked data tables may need to be updated in production environments and/or in real time. Typically, the business data objects may be manually updated, often causing disruptions to the production environment during the update process.